让模型和数据在线协同进化，自动减少32%标注成本
Info-Coevolution: An Efficient Framework for Data Model Coevolution
- 通过在线选择性标注，让模型与数据动态协同优化
- ImageNet-1K上降低32%标注与训练成本，性能不变
- 无需调参即可自动生成节省比例，适合高效数据构建场景
机器学习高度依赖数据，但现实数据持续增长带来了高效数据集构建与训练的挑战。一个核心却未解决的问题是：在现有模型与数据条件下，新数据是否需要标注或学习？传统方法保留所有数据，导致数据与训练效率不优。主动学习通过选择性标注减少冗余，但增加流程复杂度并引入偏差。本文提出Info-Coevolution框架，通过无偏的在线选择性标注，实现模型与数据的高效协同进化。利用任务特定模型（及开源模型），选择性标注并整合在线与网络数据，提升数据集效率。在ImageNet-1K等真实数据集上，该方法可降低32%的标注与训练成本，且性能无损。系统能自动给出节省比例，无需调参。结合半监督学习，标注比例可进一步降至50%。还探索了基于检索的无标签开源数据增强方法。代码已开源：https://github.com/NUS-HPC-AI-Lab/Info-Coevolution/
原文摘要 · Abstract (English)
Machine learning relies heavily on data, yet the continuous growth of real-world data poses challenges for efficient dataset construction and training. A fundamental yet unsolved question is: given our current model and data, does a new data (sample/batch) need annotation/learning? Conventional approaches retain all available data, leading to non-optimal data and training efficiency. Active learning aims to reduce data redundancy by selecting a subset of samples to annotate, while it increases pipeline complexity and introduces bias. In this work, we propose Info-Coevolution, a novel framework that efficiently enables models and data to coevolve through online selective annotation with no bias. Leveraging task-specific models (and open-source models), it selectively annotates and integrates online and web data to improve datasets efficiently. For real-world datasets like ImageNet-1K, Info-Coevolution reduces annotation and training costs by 32\% without performance loss. It is able to automatically give the saving ratio without tuning the ratio. It can further reduce the annotation ratio to 50\% with semi-supervised learning. We also explore retrieval-based dataset enhancement using unlabeled open-source data. Code is available at https://github.com/NUS-HPC-AI-Lab/Info-Coevolution/.
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